Data Visualization: Strategies, Tips, and Tools

February 10th, 2016

Matt Jansen

Digital Research Services

UNC- Chapel Hill

University Library

http://library.unc.edu/hub/

Agenda

• Why visualize? Why not?

• Visualization Principles• Human Perception

• Preattentive Processing

• Reducing Distractions

• Color

• Tips and Tricks

• Tool Landscape• Spreadsheets

• In-browser tools

• Business Intelligence

• Coding

• Design

• Q&A

About Me

• University of North Carolina at Chapel Hill• Data Analyst at University Libraries

• Background• Social Sciences and Statistics

• Supporting visualization and data analysis• Across disciplines and experience

levels• Workshops• Consultations

Why Visualize: Analysis and Dashboards

http://www.wunderground.com/

Why Visualize: Storytelling

http://upload.wikimedia.org/wikipedia/commons/2/29/Minard.png

Why Visualize: Human Cognitive Limitations

• Visual working memory is small• Numbers

• Patterns

http://www.creativebloq.com/how-design-better-data-visualisations-8134175

Why not a visualization?

• Does it beat a table?• e.g. Only a few values visualized

• Does it beat text?

• Does it oversimplify the issue? • Textual context

• Statistics

• Models never tell the whole story

Basic Principles for Visualization

• Accuracy and Human Perception

• Preattentive Processing, or ‘Pop!’

• Minimizing Distractions

• Colors

Encoding data for accuracy• Maximizing accuracy may not be

your only goal

• More dimensions = Less accuracy• Length vs. Area

• Area vs. Volume (avoid 3D effects)

Mackinlay, J. (1986) Automating the design of graphical presentations ofrelational information.ACM Trans. Graph. 5, 2 (April 1986)

Accuracy

Category Percentage

A 21.0%

B 37.0%

C 23.0%

D 19.0%

Bonus: 45 ways to communicate two quantities:http://blog.visual.ly/45-ways-to-communicate-two-quantities/

Preattentive Processing

• What ‘pops’ out in a visualization

• Survival instincts

981879263837498197949613897461394497

873218498762161799546213254989796531

859129939549719819295198197354687929

How many sevens appear above?

Preattentive processing981879263837498197949613897461394497873218498762161799546213254989796531859129939549719819295198197354687929

How many sevens appear above?

981879263837498197949613897461394497873218498762161799546213254989796531859129939549719819295198197354687929

Preattentive processing981879263837498197949613897461394497873218498762161799546213254989796531859129939549719819295198197354687929

How many sevens appear above?

981879263837498197949613897461394497873218498762161799546213254989796531859129939549719819295198197354687929

981879263837498197949613897461394497873218498762161799546213254989796531859129939549719819295198197354687929

Distractions: Data-ink Ratio

• Edward Tufte

• Within reason, maximize:𝐼𝑛𝑘 𝑢𝑠𝑒𝑑 𝑡𝑜 𝑒𝑛𝑐𝑜𝑑𝑒 𝑑𝑎𝑡𝑎 𝑣𝑎𝑙𝑢𝑒𝑠

𝑇𝑜𝑡𝑎𝑙 𝑖𝑛𝑘 𝑖𝑛 𝑣𝑖𝑠𝑢𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛

See the entire progression from the colorful, deocrative chart on the left to the simpler chart above, and beyond at: https://speakerdeck.com/cherdarchuk/remove-to-improve-the-data-ink-ratio:

Distractions: Text Orientation

http://www.allanalytics.com/author.asp?section_id=3072&doc_id=262539

• Text is often one of the most important elements of a visualization• Text should be as easily readable as possible

Color

• Don’t use Red-Green palettes• Indistinguishable to most common

forms of colorblindness

• “Get it right in black and white”

• Color is often unnecessary or distracting

• Choosing a palette:• http://colorbrewer2.org/

http://unilytics.com/archives/4533

Tips and Tricks• Draw pictures!

• Squint Test

• Small Multiples

Draw Pictures!

Dear Data projecthttp://www.dear-data.com/week-34-a-week-of-urban-wildlife

Designing a visualization of library desk traffic at UNC

Squint Test

• Preattentive Processing• Easy way to test what elements

of a visualization ‘pop’ and what draws the eye of a new viewer

http://blog.xlcubed.com/2008/08/the-dashbord-squint-test/

Small Multiples

• What you can fit in one visualization vs. what you should

• Usually, the simpler the better

• Alternatively, remove unnecessary variables

Tool Landscape

• Spreadsheets

• In-browser tools

• Business Intelligence Tools

• Coding

• Design

Spreadsheetse.g. Microsoft Excel, LibreOffice, Open Office

• Pros:

• You probably already have it

• Your data probably passes through it already

• Secure

• Already integrated in workflows

• Cons:

• Software not primarily designed for visualization

• Static and local

In-Browsere.g. Plot.ly, Datawrapper, Raw, Timeline.js

• Pros:

• Often easiest, most accessible, quickest

• Often free or cheap

• Many tools available

• Cons:

• Most subject to change (or disappearance)

• Inflexibility

• Specialized functionality

• Strict data format needs

• Dependence on other software

• Too many options

Plot.ly

In-Browser: Mappinge.g. ArcGIS Online, CartoDB

• Pros:

• Most flexible mapping tools

• Includes other map-oriented functionality

• Cons:

• Full benefits require a more advanced tool

• ArcMap

• QGIS

• No other visualization types available

ArcGIS Online

Business Intelligencee.g. Tableau, Qlik, SAS Visual Analytics

• Pros:

• Flexible, but don’t require much if any coding

• Point and click interfaces

• Good support/frequent updates

• Some free public options

• Cons:

• Most expensive

• IT support for large implementations

• Business-oriented user communities

https://public.tableau.com/s/gallery/fatal-drug-overdose-rates-united-states

Tableau

Codinge.g. JavaScript(D3.js), R(ggplot), Python

• Pros:

• Generally Free

• If you have the time to learn it

• Most flexible and powerful

• Cons:

• Multiple languages necessary

• Need to hire developer(s)

• Time-intensive

http://bl.ocks.org/mbostock/4060954

D3.js

Designe.g. Adobe Creative Suite, Inkscape

• Pros:

• Most aesthetically oriented

• Can be combined with other tools

• Cons:

• Expensive

• Not data-oriented

• ‘Infographic effect’

• Static

http://icharts.net/blogs/2013/spotlight-interview-unique-approach-infographics-journalism-alberto-cairo

Adobe Illustrator

Conclusions

• Easier tools come at a cost

• Time/Price:• Fancier is often more expensive

• Are the free tools really free?

• Ever-growing spectrum

• Combining tools and knowing your data

• How much flexibility and fine tuning do you need?

Questions?mtjansen@email.unc.edu

Learn more: Tools

• Tool lists:• http://dirtdirectory.org/

• http://selection.datavisualization.ch/

• Map Galleries• CartoDB:

https://cartodb.com/gallery/

• ArcGIS Online:

http://www.arcgis.com/home/gallery.html#c=esri&t=maps&o=avgrating

• Sample Galleries• D3:

https://github.com/mbostock/d3/wiki/Gallery

• Tableau:

https://public.tableau.com/s/gallery

• Plot.ly

https://plot.ly/feed/

Learn more: Theory PracticeEdward Tufte:The Visual Display of Quantitative

Information (2001)Visual Explanations (1997)Envisioning Information (1990)

Colin Ware:Information Visualization:

Perception for Design (2004)

Stephen Few:Show Me the Numbers (2004)Information Dashboard Design (2006)Now You See It (2009)

Alberto Cairo:The Functional Art (2012)